Purpose: A major factor in dose-fractionation selection for intracranial metastases in stereotactic radiosurgery (SRS) is the size of the target lesion and consequently the dose-volume to the surrounding normal brain tissue (NTV), as this has been correlated with brain radiation necrosis (RN). This study outlines the development and validation of a predictive model that can estimate the NTV for a range of dose-fractionation schemes based on target diameter from a patient's MRI. Methods: Data from a cohort of historical SRS clinical treatment plans were used to extract three key input parameters for the model - conformity index, gradient index, and a scaling factor which were then defined as a function of target volume. The relationship between the measured tumour diameter and the NTV was established by approximating the target to a spherical volume covered by the prescription dose. A scaling factor (λNTV) describes the non-linear fall-off of dose beyond the target. This was then used to provide a first-order approximation of the resulting NTV. The predictive model was retrospectively validated using linear regression against actual NTV values from 39 historical SRS plans which were independent to the derivation process. The model was validated for both three-dimensional (3D) target diameter and axial-only two-dimensional (2D) estimates of target diameter values. Results: The prediction model directly relates lesion diameter to NTV volume (cc) and thus RN risk for a given dose-fractionation. The predicted NTV (cc) for both 3D- and 2D-based volume estimates could statistically significantly predict the actual NTV (cc): R2=0.942 (p<.0005) for 3D-based estimate, and R2=0.911 (p=<.0005) for axial-only 2D-based estimate. Conclusion: This knowledge-based method for NTV prediction in intracranial SRS provides the clinician with a decision support tool to appropriately select dose-fractionation prior to treatment planning.
Purpose: A major factor in dose-fractionation selection for intracranial metastases in stereotactic radiosurgery (SRS) is the size of the target lesion and consequently the dose-volume to the surrounding normal brain tissue (NTV), as this has been correlated with brain radiation necrosis (RN). This study outlines the development and validation of a predictive model that can estimate the NTV for a range of dose-fractionation schemes based on target diameter from a patient's MRI. Methods: Data from a cohort of historical SRS clinical treatment plans were used to extract three key input parameters for the model - conformity index, gradient index, and a scaling factor which were then defined as a function of target volume. The relationship between the measured tumour diameter and the NTV was established by approximating the target to a spherical volume covered by the prescription dose. A scaling factor (λNTV) describes the non-linear fall-off of dose beyond the target. This was then used to provide a first-order approximation of the resulting NTV. The predictive model was retrospectively validated using linear regression against actual NTV values from 39 historical SRS plans which were independent to the derivation process. The model was validated for both three-dimensional (3D) target diameter and axial-only two-dimensional (2D) estimates of target diameter values. Results: The prediction model directly relates lesion diameter to NTV volume (cc) and thus RN risk for a given dose-fractionation. The predicted NTV (cc) for both 3D- and 2D-based volume estimates could statistically significantly predict the actual NTV (cc): R2=0.942 (p<.0005) for 3D-based estimate, and R2=0.911 (p=<.0005) for axial-only 2D-based estimate. Conclusion: This knowledge-based method for NTV prediction in intracranial SRS provides the clinician with a decision support tool to appropriately select dose-fractionation prior to treatment planning.
Authors: Alexander R Delaney; Jim P Tol; Max Dahele; Johan Cuijpers; Ben J Slotman; Wilko F A R Verbakel Journal: Int J Radiat Oncol Biol Phys Date: 2015-11-10 Impact factor: 7.038
Authors: Marco Fusella; Alessandro Scaggion; Nicola Pivato; Marco Andrea Rossato; Alessandra Zorz; Marta Paiusco Journal: Med Phys Date: 2018-04-19 Impact factor: 4.071
Authors: Eric L Chang; Jeffrey S Wefel; Kenneth R Hess; Pamela K Allen; Frederick F Lang; David G Kornguth; Rebecca B Arbuckle; J Michael Swint; Almon S Shiu; Moshe H Maor; Christina A Meyers Journal: Lancet Oncol Date: 2009-10-02 Impact factor: 41.316
Authors: Mark E Linskey; David W Andrews; Anthony L Asher; Stuart H Burri; Douglas Kondziolka; Paula D Robinson; Mario Ammirati; Charles S Cobbs; Laurie E Gaspar; Jay S Loeffler; Michael McDermott; Minesh P Mehta; Tom Mikkelsen; Jeffrey J Olson; Nina A Paleologos; Roy A Patchell; Timothy C Ryken; Steven N Kalkanis Journal: J Neurooncol Date: 2009-12-04 Impact factor: 4.130